Vehicle license plate detection using region-based convolutional neural networks

  • Muhammad Aasim Rafique
  • Witold Pedrycz
  • Moongu Jeon
Methodologies and Application


Vehicle license plate (LP) detection is a relatively complex problem until we assume the use of a static camera, variations in illumination, known templates of the LP, guaranteed color patterns and other simple assumptions. Practical applications demand robust and generalized LP detection techniques to accommodate complex scenarios. This work suggests a new approach to solving this problem by treating the vehicle LP as an object. The primary focus of this study is to address following tasks associated with the challenge of LP detection: (1) LP detection in every frame of a video sequence, (2) detection of partial LPs and (3) detection of LPs with moving cameras and moving vehicles. The state-of-the-art object detection techniques, including convolutional neural networks with region proposal (RCNN), its successors (Fast-RCNN and Faster-RCNN) and the exemplar-SVM, are used in this work to provide solutions to the problem. The suggested study demonstrates better results in comprehensive tests and comparisons than other conventional approaches.


Vehicle license plate detection RCNN exemplar-SVM Artificial neural networks 



This work was supported by the Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIP) (No. B0101-15-0525, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Muhammad Aasim Rafique
    • 1
  • Witold Pedrycz
    • 2
    • 3
    • 4
  • Moongu Jeon
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceGwangju Institute of Science and Technology (GIST)GwangjuRepublic of Korea
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  4. 4.Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

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